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Shrinkage (statistics)

Shrinkage Priors for Bayesian Penalized Regression
Shrinkage Estimation of Rate Statistics Arxiv:1810.07654V1 [Stat.AP] 17 Oct
Linear Methods for Regression and Shrinkage Methods
Linear, Ridge Regression, and Principal Component Analysis
Kernel Mean Shrinkage Estimators
Generalized Shrinkage Methods for Forecasting Using Many Predictors
Cross-Validation, Shrinkage and Variable Selection in Linear Regression Revisited
Mining Big Data Using Parsimonious Factor, Machine Learning, Variable Selection and Shrinkage Methods Hyun Hak Kim1 and Norman R
Shrinkage Improves Estimation of Microbial Associations Under Di↵Erent Normalization Methods 1 2 1,3,4, 5,6,7, Michelle Badri , Zachary D
Shrinkage Estimation for Functional Principal Component Scores, with Application to the Population Kinetics of Plasma Folate
Image Denoising Via Residual Kurtosis Minimization
An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Deﬁnite Matrices∗†
Arxiv:1602.01182V2 [Stat.ML] 5 Feb 2017
Optimal Shrinkage Estimation of Mean Parameters in Family of Distributions with Quadratic Variance
Does Cross-Validation Work When P ≫ N?
Shrinkage and Penalized Likelihood As Methods to Improve Predictive Accuracy
Dimension Reduction and Shrinkage Methods for High Dimensional Disease Risk Scores in Historical Data
Machine Learning, Shrinkage Estimation, and Economic Theory

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Computation of Regularized Linear Discriminant Analysis
Lecture 13: Principal Components Analysis Statistical Learning (BST 263)
Shrinkage Regression
Robust Shrinkage Estimation of High-Dimensional Covariance Matrices 1Yilun Chen, 2Ami Wiesel, 1Alfred O
Mining Big Data Using Parsimonious Factor and Shrinkage Methods Hyun Hak Kim1 and Norman R
Econ 2148, Fall 2017 Shrinkage in the Normal Means Model
Ridge Regression, the Lasso
Optimal Shrinkage Estimation in Heteroscedastic Hierarchical Linear Models
Kurtosis Control in Wavelet Shrinkage with Generalized Secant Hyperbolic Prior
Mean Shrinkage Improves the Classification of ERP Signals By
Understanding Shrinkage Estimators: from Zero to Oracle to James-Stein
Bayesian Shrinkage Methods for High-Dimensional Regression
Overfitting in Regression-Type Models
Univariate Shrinkage in the Cox Model for High Dimensional Data
Modular Meta-Learning with Shrinkage
Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities
Dynamic Shrinkage Estimation of the High-Dimensional Minimum-Variance Portfolio
Efficient Shrinkage in Parametric Models

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